Friday, 6 March 2026

1. Super Market Web Page using Internal CSS


<!DOCTYPE html>
<html>
<head>
<title>Super Market</title>

<style>
body{
font-family: Arial;
background-color: #f2f2f2;
}

h1{
color: white;
background-color: green;
text-align: center;
padding: 10px;
}

.container{
width: 80%;
margin: auto;
}

.product{
background-color: white;
padding: 10px;
margin: 10px;
border: 1px solid gray;
}

</style>

</head>

<body>

<h1>Fresh Super Market</h1>

<div class="container">

<div class="product">
<h3>Rice</h3>
<p>Price: ₹60 per kg</p>
</div>

<div class="product">
<h3>Milk</h3>
<p>Price: ₹50 per litre</p>
</div>

<div class="product">
<h3>Bread</h3>
<p>Price: ₹30</p>
</div>

<div class="product">
<h3>Apples</h3>
<p>Price: ₹120 per kg</p>
</div>

<div class="product">
<h3>Eggs</h3>
<p>Price: ₹6 per egg</p>
</div>

</div>

</body>
</html>

2. Resume using Inline CSS


<!DOCTYPE html>
<html>
<head>
<title>My Resume</title>
</head>

<body style="font-family: Arial; background-color: #f4f4f4;">

<h1 style="color: blue;">Manisha Bhardwaj</h1>

<p style="font-size:18px;">Email: manisha@email.com</p>
<p style="font-size:18px;">Phone: 9876543210</p>

<h2 style="color: green;">Career Objective</h2>
<p style="font-size:16px;">To work in a challenging environment where I can use my skills and knowledge.</p>

<h2 style="color: green;">Education</h2>
<ul style="font-size:16px;">
<li>B.Tech / MCA</li>
<li>12th from CBSE</li>
<li>10th from CBSE</li>
</ul>

<h2 style="color: green;">Skills</h2>
<ul style="font-size:16px;">
<li>HTML</li>
<li>CSS</li>
<li>Python</li>
</ul>

<h2 style="color: green;">Hobbies</h2>
<ul style="font-size:16px;">
<li>Reading</li>
<li>Music</li>
<li>Traveling</li>
</ul>

</body>
</html>

Friday, 20 February 2026

CSS and type of CSS

1. Inline CSS (The "Direct Note")This is written directly inside the HTML tag.

It’s like putting a sticker right on an object.Best for: Changing just one specific thing very quickly.Example:

HTML

I am Blue!

2. Internal CSS (The "Instruction List")This is written at the top of your HTML file inside a

3. External CSS (The "Separate Book")This is the most professional way.

You create a completely separate file (ending in .css) and link it to your HTML.

Best for:

Big websites with many pages.

You can change the color of 100 pages at once just by changing one line in the CSS file!

Example:In your HTML: In your style.css file: body { background-color: lightgreen; }

EXAMPLE OF CSS AND HTML

Project Name: The Season Watcher 3000

The Real-Life Question: "How can we create a dashboard that shows the current season, but makes it look like a high-tech app where the buttons glow and the boxes pop out when you touch them?"

The HTML File (`index.html`)

This is the Structure. We are creating a "Container" to hold our four "Season Cards."

HTML
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Season Watcher 3000</title>
    <link rel="stylesheet" href="style.css">
</head>
<body>

    <h1 class="main-title">Smart Home: Season Monitor</h1>

    <div class="dashboard">
        <div class="card" id="spring">
            <div class="icon">🌸</div>
            <h2>Spring</h2>
            <p>Status: Blooming</p>
            <button class="btn">View Garden</button>
        </div>

        <div class="card" id="summer">
            <div class="icon">☀️</div>
            <h2>Summer</h2>
            <p>Status: Sunny</p>
            <button class="btn">Check AC</button>
        </div>

        <div class="card" id="autumn">
            <div class="icon">🍂</div>
            <h2>Autumn</h2>
            <p>Status: Windy</p>
            <button class="btn">Clear Leaves</button>
        </div>

        <div class="card" id="winter">
            <div class="icon">❄️</div>
            <h2>Winter</h2>
            <p>Status: Freezing</p>
            <button class="btn">Heat On</button>
        </div>
    </div>

</body>
</html>

2. The External CSS File (`style.css`)

This is the Style. We use "Flexbox" to align the boxes and "Transitions" to make them move smoothly.

CSS
/* General Page Setup */
body {
    background-color: #1a1a2e; /* Dark "Space" Blue */
    color: white;
    font-family: 'Segoe UI', sans-serif;
    display: flex;
    flex-direction: column;
    align-items: center;
    padding: 50px;
}

.main-title {
    margin-bottom: 40px;
    text-transform: uppercase;
    letter-spacing: 3px;
    border-bottom: 2px solid #0f3460;
}

/* The Grid/Dashboard Layout */
.dashboard {
    display: flex;
    gap: 20px;
    flex-wrap: wrap;
    justify-content: center;
}

/* Designing the Individual Cards */
.card {
    background-color: #16213e;
    border: 2px solid #0f3460;
    border-radius: 20px;
    padding: 30px;
    width: 200px;
    text-align: center;
    /* This makes the animation smooth! */
    transition: all 0.4s ease;
}

/* Real-Life Interaction: The Hover Effect */
.card:hover {
    transform: scale(1.1) rotate(2deg); /* Grows and tilts slightly */
    border-color: #e94560;
    box-shadow: 0px 10px 30px rgba(233, 69, 96, 0.5);
}

.icon {
    font-size: 50px;
    margin-bottom: 10px;
}

/* Making the Buttons Glow */
.btn {
    background-color: #e94560;
    color: white;
    border: none;
    padding: 10px 20px;
    border-radius: 50px;
    cursor: pointer;
    font-weight: bold;
    margin-top: 15px;
}

.btn:hover {
    background-color: white;
    color: #e94560;
}

What makes this a "Good" Demo?

The "Pop" (Scale): In the CSS, transform: scale(1.1) makes the card grow when you touch it with the mouse. This feels like a modern app.
The "Glow" (Box-Shadow): Using box-shadow on hover makes the card look like it’s lighting up.
Flexbox: The display: flex command in the .dashboard section acts like a magnet, pulling all the cards into a neat line automatically.

Thursday, 19 February 2026

HTML CODE FOR DISPLAYING 4 DIFFERENT HTML PAGES ON SINGLE HTML PAGE USING FRAMES

To display four separate HTML files on a single page using a "frames" approach, we use the tag. This is the classic way to split a browser window into multiple independent sections. Since replaces the tag, this "Master" file will pull the content from four other files you create. 1. The Master File (index.html) This file defines the layout. It creates two rows and two columns. The Four Seasons - Master Frame <body> Your browser does not support frames. Please update your browser. </body> HTML

The Four Seasons - Master Frame <body> Your browser does not support frames. Please update your browser. </body> 2. The Four Content Files You must create these four files and save them in the same folder as your index.html.

<body>

Your browser does not support frames. Please update your browser.

</body>

</noframes>

</frame></frame></frame></frame></frameset>

File 1: spring.html HTML

Spring Season

Spring is a time of rebirth. Flowers bloom and the weather turns mild.

File 2: summer.html HTML

Summer Season

Summer is the hottest season. It is perfect for vacations and swimming.

File 3: autumn.html HTML

Autumn Season

In Autumn, leaves change color to gold and red before falling.

File 4: winter.html HTML

Winter Season

Winter brings snow and ice. It is the coldest time of the year.

How to run this: Create a folder on your computer.

Save the first block of code as index.html.

Save the four season codes as spring.html, summer.html, autumn.html, and winter.html.

Open index.html with your browser.

Key Attributes Used: rows="50%,50%":

Splits the screen horizontally into two equal halves. cols="50%,50%":

Splits the screen vertically into two equal halves.

noresize: Prevents the user from dragging the borders to change frame sizes.

scrolling="yes": Adds a scrollbar if your content or image is too large for the frame.

Monday, 9 February 2026

Minor Project Ideas for B.tech Computer Science

B.Tech Minor Project: Data Science using Regression

This project uses Linear Regression for continuous prediction and Logistic Regression for binary classification problems such as pass/fail and disease prediction.

MINOR PROJECT IDEAS

1. Heart Disease Prediction

Problem:
Classify if a person has heart disease.

Inputs:

Age
BP
Cholesterol
Heart rate

Output:

Disease / No disease
Heart Disease Prediction

Problem:
Classify if a person has heart disease.

Inputs:
- Age
- BP
- Cholesterol
- Heart rate
Output:

Disease / No disease

2. College Placement Probability Prediction System

(Highly impressive for viva)

Problem

Predict whether a student will get placed + expected salary range.

Models

Logistic Regression → placed / not placed
Linear Regression → expected package

Features

CGPA trend
Coding test scores
Internship experience
Soft-skill ratings

Extra Credit

Feature importance analysis
Probability confidence score

3. Financial Credit Risk & EMI Recommendation System

(Industry-oriented)

Problem

Predict:

Loan approval probability
Safe EMI amount

Models

Logistic Regression → loan approval
Linear Regression → EMI amount

Features

Income trend
Expenses
Credit behavior
Loan tenure

BIG Add-On

Risk tiers (Low / Medium / High)

4. Smart Energy Consumption Forecasting System

(Engineering + Sustainability)

Problem

Forecast electricity consumption & detect over-usage risk.

Models

Linear Regression → energy units
Logistic Regression → overload risk

Features

Appliance usage
Seasonal effects
Household size

Outputs

Monthly forecast
Warning alerts

5. Smart Traffic Congestion & Accident Risk System

(Engineering + AI)

Problem

Predict:

Traffic congestion level
Accident probability

Models

Linear Regression → congestion index
Logistic Regression → accident risk

Features

Vehicle count
Time of day
Weather

6. Social Media Misinformation Risk Analyzer

(Trending & Research-oriented)

Problem

Predict whether content is misleading.

Models

Logistic Regression → fake / real
Linear Regression → virality score

Features

Engagement metrics
Posting time
Account credibility

BIG VALUE

Explainable coefficients
Ethical AI discussion

7. CROP DISEASE PREDICTION SYSTEM

(Data Science Project using Logistic Regression)

🔹 . Problem Statement

Early detection of crop diseases is critical to reduce yield loss and improve agricultural productivity.
This project aims to predict whether a crop is diseased or healthy based on environmental and crop-related parameters using Logistic Regression.

🔹 . Why this project is “BIG & GOOD”

Real-world agricultural problem
Social + economic impact
Explainable ML (important for farmers)
Can be extended to yield loss prediction
Faculty-friendly & industry-relevant

🔹 . Project Objectives

Predict disease presence (Yes/No)
Analyze factors causing disease
Provide early warning
(Optional) Predict severity or yield loss

🔹 . Dataset (Non-image, Data Science based)

Input Features (examples)

Temperature (°C)
Humidity (%)
Rainfall (mm)
Soil moisture
Soil pH
Crop type
Season
Fertilizer usage
Pesticide usage

Output

Disease (0 = Healthy, 1 = Diseased)

📌 Datasets:

Kaggle: Crop Disease / Agriculture datasets
Government agriculture data
Synthetic dataset (acceptable for minor project)

🔹 . Machine Learning Models Used

✅ Logistic Regression (Main Model)

Used because:

Output is binary
Easy to interpret coefficients
Works well with tabular data

Equation:

P(\text{Disease}) = \frac{1}{1 + e^{-(\beta_0 + \beta_1x_1 + ... + \beta_nx_n)}}

(Optional) Linear Regression

Predict severity level
Predict expected yield loss

🔹 . System Architecture

Data Collection
Data Preprocessing
Feature Selection
Logistic Regression Model
Prediction
Result Visualization
Recommendation System

🔹 . Implementation Flow (Python)

Load dataset
Handle missing values
Train-test split
Train Logistic Regression model
Evaluate using:
- Accuracy
- Confusion Matrix
- Precision, Recall
Plot:
- Probability curve
- Feature importance

🔹 . Results to Show (VERY IMPORTANT)

Disease prediction accuracy
Confusion matrix
Probability vs threshold graph
Feature impact analysis
Sample predictions

🔹 . Future Scope (Makes project BIG)

Image-based disease detection (CNN)
IoT sensor integration
Mobile app for farmers
Real-time weather API
Crop recommendation system

10.Intelligent Crop Yield Forecasting System

(Different from disease prediction)

Problem:
Predict crop yield before harvest.

Models:

Linear Regression → yield (tons/hectare)
Logistic Regression → low / normal yield risk

Features:
Rainfall, soil nutrients, fertilizer, season

11. Air Pollution Level Prediction & Health Risk Alert

Problem:
Predict AQI and classify health risk.

Models:

Linear Regression → AQI value
Logistic Regression → hazardous / safe

Features:
PM2.5, PM10, NO₂, SO₂, temperature

Monday, 2 February 2026

DATA SCIENCE LAB MANUAL

Example 1: Linear Regression

PREDITING PRICE BASES ON AREA

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# 1. Example Data
# X = Input (Area of house)
# Y = Output (Price)
X = np.array([100, 200, 300, 400, 500]).reshape(-1, 1)
Y = np.array([10, 20, 30, 40, 50])

# 2. Create and train the model
model = LinearRegression()   # intercept is included by default
model.fit(X, Y)

# 3. Get slope and intercept
m = model.coef_[0]        # slope
b = model.intercept_      # intercept

# 4. Prediction
x_new = 350
y_pred = model.predict([[x_new]])

# 5. Plot the graph
X_line = np.linspace(100, 500, 100).reshape(-1, 1)
Y_line = model.predict(X_line)

plt.scatter(X, Y)          # original data points
plt.plot(X_line, Y_line)   # regression line
plt.scatter(x_new, y_pred) # predicted point
plt.xlabel("Area")
plt.ylabel("Price")
plt.title("Simple Linear Regression")
plt.show()

# 6. Print results
print("Slope (m):", m)
print("Intercept (b):", b)
print("Equation: Y =", m, "* X +", b)
print("Predicted Price for", x_new, ":", y_pred[0])

Example 2: Linear Regression (Study Hours vs Marks)

👉 Problem Statement

Predict student marks based on study hours.

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

# Data

X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) # Study hours

Y = np.array([35, 40, 50, 60, 70]) # Marks

# Model

model = LinearRegression()

model.fit(X, Y)

# Slope and intercept

m = model.coef_[0]

b = model.intercept_

# Prediction

hours = 6

predicted_marks = model.predict([[hours]])

# Plot

X_line = np.linspace(1, 6, 100).reshape(-1, 1)

Y_line = model.predict(X_line)

plt.scatter(X, Y)

plt.plot(X_line, Y_line)

plt.scatter(hours, predicted_marks)

plt.xlabel("Study Hours")

plt.ylabel("Marks")

plt.title("Linear Regression: Study Hours vs Marks")

plt.show()

# Output

print("Slope (m):", m)

print("Intercept (b):", b)

print("Equation: Y =", m, "* X +", b)

print("Predicted Marks for", hours, "hours:", predicted_marks[0])

2. Logistic Regression code

Program 1: Student Pass / Fail Prediction

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score, confusion_matrix

# Dataset

data = {

'Hours_Studied': [1,2,3,4,5,6,7,8],

'Passed': [0,0,0,0,1,1,1,1]

}

df = pd.DataFrame(data)

# Features and target

X = df[['Hours_Studied']]

y = df['Passed']

# Train-test split

X_train, X_test, y_train, y_test = train_test_split(

X, y, test_size=0.25, random_state=0

)

# Model

model = LogisticRegression()

model.fit(X_train, y_train)

# Prediction

y_pred = model.predict(X_test)

# Results

print("Accuracy:", accuracy_score(y_test, y_pred))

print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

print("Intercept (β0):", model.intercept_)

print("Coefficient (β1):", model.coef_)

OUTPUT

Accuracy: 1.0 Confusion Matrix: [[1 0] [0 1]] Intercept (β0): [-4.4350521] Coefficient (β1): [[1.01750279]]

Program 2: Salary > 50K Prediction (Binary Classification)


# Logistic Regression – Example 2 (Salary Prediction)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Dataset
data = {
    'Age': [22,25,30,35,40,45,50,55],
    'Experience': [0,1,3,5,7,10,15,20],
    'High_Salary': [0,0,0,1,1,1,1,1]
}

df = pd.DataFrame(data)

# Features and target
X = df[['Age', 'Experience']]
y = df['High_Salary']

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=1
)

# Model
model = LogisticRegression()
model.fit(X_train, y_train)

# Prediction
y_pred = model.predict(X_test)

# Results
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Intercept (β0):", model.intercept_)
print("Coefficients (β1, β2):", model.coef_)

Accuracy: 0.6666666666666666
Intercept (β0): [-13.33988536]
Coefficients (β1, β2): [[0.45410297 0.17502853]]

Monday, 12 January 2026

Blockchain technology

Blockchain technology is a decentralized, distributed digital ledger that securely records transactions across many computers, creating an immutable (unchangeable) and transparent record of data, like ownership or transactions, without needing a central authority like a bank. Data is grouped into "blocks," cryptographically linked in a chronological "chain," and validated by network consensus, making it highly secure and resistant to tampering.

How it works:

Blocks & Chains: Transactions are bundled into blocks. When a block fills, it's sealed with a unique digital fingerprint (cryptographic hash) and linked to the previous block, forming a chain.
Decentralization: Instead of one central server, copies of the ledger are held by many computers (nodes) on the network, removing single points of failure.

Consensus: Before a new block is added, most nodes must agree on its validity through a consensus mechanism, ensuring trust.

Immutability: Once a block is added, altering its data would require changing all subsequent blocks across the majority of the network, which is computationally infeasible.

Key Features:

Transparency: All participants can view transactions.
Security: Cryptography and decentralization make it tamper-proof.
No Intermediaries: Reduces reliance on third parties, lowering costs and increasing efficiency.

Uses:

Cryptocurrencies: Like Bitcoin, to track digital money.
Supply Chain: To track goods transparently.
Smart Contracts: Self-executing contracts with terms directly written into code.
Digital Identity & Voting: Securing personal data and votes.

Security in blockchain technology involves addressing vulnerabilities at multiple layers, from the underlying network infrastructure to the application-specific smart contract code. Key issues include consensus manipulation, smart contract logic flaws, and external operational risks.

Blockchain-Related Issues

These vulnerabilities target the foundational infrastructure and protocols of the blockchain network.

51% Attacks: An attacker or group gains control of more than 50% of the network's total computing power (hash power in Proof of Work) or staked tokens (in Proof of Stake), allowing them to manipulate transaction ordering, prevent new transactions from being confirmed, and perform "double-spending" of coins.

Consensus Mechanism Attacks: Issues in consensus algorithms (e.g., selfish mining, block withholding) can be exploited to gain a disproportionate amount of block rewards or disrupt network operations.
Network Attacks: The network layer is vulnerable to standard network threats like Distributed Denial of Service (DDoS) attacks, Sybil attacks (creating multiple fake identities), and routing attacks that can intercept or modify data transmission between nodes.
Private Key Security: A primary operational risk is the compromise or mismanagement of users' private keys, which control access to digital assets. This often occurs through phishing or insecure storage, as transactions are irreversible once signed with a private key.

Higher-Level Language (Solidity) Related Issues

These issues stem from vulnerabilities and anti-patterns in the smart contract programming language itself, such as Solidity.

Reentrancy: A malicious contract repeatedly calls a vulnerable contract's function before the first execution is complete, often to drain funds. The "Checks-Effects-Interactions" pattern is the standard mitigation.
Arithmetic Overflows/Underflows: Fixed-size integer types in Solidity (before version 0.8.0, which added automatic checks) can lead to values wrapping around their maximum or minimum limits, resulting in incorrect calculations and potential fund manipulation.
Unauthorized Access: Missing or improperly implemented access control modifiers (like onlyOwner) can allow unauthorized users to execute critical functions.
Insecure Randomness: Smart contracts rely on deterministic processes, making true randomness difficult to achieve on-chain. Using predictable variables like block.timestamp or blockhash for random numbers can be exploited by miners or attackers.
Front-Running: Attackers monitor the mempool (where unconfirmed transactions wait) for profitable transactions and submit their own transaction with a higher gas fee to have it processed first, gaining an unfair advantage.

EVM Bytecode Related Issues

Vulnerabilities at the Ethereum Virtual Machine (EVM) bytecode level are related to how the compiled code executes.

Stack Overflows/Underflows: The EVM uses a stack for computation; if the stack limits are exceeded due to recursive calls or complex logic, the contract can crash or behave unexpectedly.
Call Stack Depth Limit: The EVM has a call stack depth limit. Maliciously crafted contracts can exploit this to cause a denial-of-service condition for other contracts.
Short Address Attack: An attacker can manipulate transaction input data to exploit insufficient address length checks, potentially redirecting funds to their own address or causing other errors.
Unchecked External Calls: If a smart contract calls another contract without verifying the return value, a failed external call may not revert the entire transaction, leading to inconsistent state in the calling contract.

Real-Life Attacks on Blockchain Applications/Smart Contracts

The DAO Hack (2016): An attacker exploited a reentrancy vulnerability in the smart contract to drain approximately $60 million worth of Ether. This event led to a contentious hard fork, splitting Ethereum into Ethereum (ETH) and Ethereum Classic (ETC).
Parity Wallet Hack (2017): A bug in a multi-signature wallet library left two critical functions public, allowing an attacker to claim ownership of the contract and freeze about $150 million worth of Ether indefinitely.
Ronin Bridge Hack (2022): Attackers compromised the private keys of five out of nine validator nodes for the Ronin Network's cross-chain bridge, enabling them to forge withdrawal transactions and steal over $600 million in one of the largest DeFi hacks to date.
Mt. Gox Exchange Collapse (2014): This was an attack on a centralized exchange, but it highlighted the vulnerability of centralized entities within the broader crypto ecosystem. Attackers exploited a Bitcoin transaction malleability vulnerability, leading to the loss of millions in user funds and the exchange's bankruptcy.

Trusted Execution Environments (TEEs)

Trusted Execution Environments (TEEs), such as Intel SGX, are a potential solution for enhancing blockchain security and privacy.

Function: TEEs create a secure, isolated environment (enclave) within a processor where data and code can be executed with integrity and confidentiality, even if the host operating system or network is compromised.
Benefits for Blockchain:
- Privacy: TEEs can process private or sensitive data off-chain within the enclave without revealing the raw data to the public ledger or even the node operator, thus enhancing data privacy.
- Security: By isolating smart contract execution, TEEs can protect against certain host-level attacks and ensure the integrity of the computation, even if a node is malicious.
- Scalability: Offloading complex computations to TEEs can potentially reduce the load on the main blockchain, improving scalability and transaction speeds.
Limitations and Issues: TEEs themselves can have vulnerabilities (e.g., side-channel attacks). Furthermore, their use introduces a degree of reliance on hardware manufacturers (centralization risk) and increases the overall complexity of the system design.

Blockchain security encompasses vulnerabilities at protocol, language (Solidity), and runtime (EVM) levels, alongside real-world exploits and hardware mitigations like Trusted Execution Environments (TEEs). These issues arise due to blockchain's immutability, public nature, and economic incentives for attacks. Detailed notes follow with examples from common lectures on smart contract security.

Blockchain Issues

Blockchain platforms face consensus, scalability, and economic attacks.

51% Attacks: Miner majority rewrites history; example—Ethereum Classic (ETC) lost $1.1M in 2019 as attackers double-spent via majority hash power.
Sybil Attacks: Flooding network with fake nodes; countered by Proof-of-Stake (PoS) in Ethereum 2.0.
Eclipse Attacks: Isolating nodes to manipulate views; risks private chain forks.

Solidity Issues

Solidity, Ethereum's primary language, introduces high-level pitfalls exploitable due to its Turing-completeness and lack of safe defaults.

Reentrancy: External calls before state updates allow recursive calls; DAO hack (2016) drained $60M by reentering withdrawal function.
Integer Overflow/Underflow: Pre-0.8.0, unchecked math wraps values; example—attackers mint extra tokens via uint overflow in bad ERC-20.
Access Control Flaws: Missing modifiers like onlyOwner; unprotected functions let anyone self-destruct contracts.
Timestamp Dependence: block.timestamp miner-manipulable; used in gambling contracts for predictable "randomness."
Front-Running: Mempool scanning to bid higher gas; DEX arbitrage bots frontrun trades for profit.

EVM Bytecode Issues

EVM executes low-level bytecode, exposing gas mechanics and opcodes to abuse.

Gas Limit DoS: Loops over unbounded arrays exhaust block gas (30M on Ethereum); attackers submit transactions failing late.
Unchecked Calls: call() returns false on failure but doesn't revert; leads to silent fund sends.
Delegatecall Risks: Context swaps enable storage overwrites; Parity Wallet (2017) lost $30M via delegatecall bug initializing wrong library.
Opcode Limits: STATICCALL post-Constantinople prevents state changes in view functions.

Real-Life Attacks

Historical exploits highlight patterns.

The DAO (2016): Reentrancy; led to Ethereum hard fork.
Parity Multi-Sig (2017): Self-destruct vulnerability; $280K lost.
BeautyChain (BEC, 2018): Infinite mint via balances[msg.sender] += totalSupply overflow.
bZx Flash Loans (2020): Price oracle manipulation via repeated trades in one tx.
Cream Finance (2021): Flash loan + oracle exploit drained $130M.

Attack	Cause	Loss	Fix
DAO	Reentrancy	$60M	Checks-Effects-Interactions
Parity	Delegatecall	$280K	Initialization guards
BEC	Overflow	Millions	SafeMath/OpenZeppelin

Trusted Execution Environments

TEEs provide hardware-based isolation for off-chain computation, enhancing blockchain privacy/scalability.

SGX (Intel): Enclaves shield code/data from host OS; Secret Network uses for private smart contracts.
Issues: Side-channels (Spectre/Meltdown), attestation trust; example—Malware in SGX enclave compromised keys.
Use Cases: Oracle feeds (e.g., Phala Network), ZK proofs generation without revealing inputs.
Alternatives: AWS Nitro Enclaves, ARM TrustZone for mobile blockchain apps.

Topics

Friday, 6 March 2026

1. Super Market Web Page using Internal CSS

2. Resume using Inline CSS

Friday, 20 February 2026

I am Blue!

Project Name: The Season Watcher 3000

The HTML File (index.html)

2. The External CSS File (style.css)

What makes this a "Good" Demo?

Thursday, 19 February 2026

Spring Season

Summer Season

Autumn Season

Winter Season

Monday, 9 February 2026

B.Tech Minor Project: Data Science using Regression

1. Heart Disease Prediction

Heart Disease Prediction

2. College Placement Probability Prediction System

Problem

Models

Features

Extra Credit

3. Financial Credit Risk & EMI Recommendation System

Problem

Models

Features

BIG Add-On

4. Smart Energy Consumption Forecasting System

Problem

Models

Features

Outputs

5. Smart Traffic Congestion & Accident Risk System

Problem

Models

Features

6. Social Media Misinformation Risk Analyzer

Problem

Models

Features

BIG VALUE

7. CROP DISEASE PREDICTION SYSTEM

🔹 . Problem Statement

🔹 . Why this project is “BIG & GOOD”

🔹 . Project Objectives

🔹 . Dataset (Non-image, Data Science based)

Input Features (examples)

Output

🔹 . Machine Learning Models Used

✅ Logistic Regression (Main Model)

(Optional) Linear Regression

🔹 . System Architecture

🔹 . Implementation Flow (Python)

🔹 . Results to Show (VERY IMPORTANT)

🔹 . Future Scope (Makes project BIG)

10.Intelligent Crop Yield Forecasting System

11. Air Pollution Level Prediction & Health Risk Alert

Monday, 2 February 2026

Example 1: Linear Regression

Example 2: Linear Regression (Study Hours vs Marks)

👉 Problem Statement

Program 1: Student Pass / Fail Prediction

Program 2: Salary > 50K Prediction (Binary Classification)

Monday, 12 January 2026

Blockchain Issues

Solidity Issues

EVM Bytecode Issues

Real-Life Attacks

Trusted Execution Environments

The HTML File (`index.html`)

2. The External CSS File (`style.css`)